Pith. sign in

REVIEW 9 cited by

MaskBit: Embedding-free Image Generation via Bit Tokens

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.16211 v2 pith:DDJEWDMG submitted 2024-09-24 cs.CV cs.LG

Mark Weber , Lijun Yu , Qihang Yu , Xueqing Deng , Xiaohui Shen , Daniel Cremers , Liang-Chieh Chen This is my paper · ORCID
classification cs.CVcs.LG
keywords imagegenerationmodeltokensembedding-freespacevqgancontribution
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Masked transformer models for class-conditional image generation have become a compelling alternative to diffusion models. Typically comprising two stages - an initial VQGAN model for transitioning between latent space and image space, and a subsequent Transformer model for image generation within latent space - these frameworks offer promising avenues for image synthesis. In this study, we present two primary contributions: Firstly, an empirical and systematic examination of VQGANs, leading to a modernized VQGAN. Secondly, a novel embedding-free generation network operating directly on bit tokens - a binary quantized representation of tokens with rich semantics. The first contribution furnishes a transparent, reproducible, and high-performing VQGAN model, enhancing accessibility and matching the performance of current state-of-the-art methods while revealing previously undisclosed details. The second contribution demonstrates that embedding-free image generation using bit tokens achieves a new state-of-the-art FID of 1.52 on the ImageNet 256x256 benchmark, with a compact generator model of mere 305M parameters. The code for this project is available on https://github.com/markweberdev/maskbit.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Token-Level Cross-Entropy: Fr\'echet Distributional Post-Training for Autoregressive Image Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    FD-loss post-training with detached rollout replay and a probability-level straight-through estimator improves FID and FD_r6 across eight ImageNet configurations.

  2. Twins: Learn to Predict Unified Representations with Focal Loss

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Channel-wise concatenation of SigLIP2 and Flux VAE features into one token, trained with a focal-style flow-matching loss, yields a unified representation with 1.59 gFID on ImageNet 256 and VAE-level reconstruction.

  3. ELT: Elastic Looped Transformers for Visual Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Weight-shared looped transformers trained with intra-loop self-distillation match MaskGIT-class FID/FVD at roughly 4x fewer parameters and support any-time inference across loop counts.

  4. Quantize-then-Rectify: Efficient VQ-VAE Training

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A pretrained VAE can be converted into a high-compression VQ-VAE by freezing it and training only a multi-group quantizer plus a post rectifier, cutting training cost by over two orders of magnitude while keeping rFID...

  5. FreeLoRA: Enabling Training-Free LoRA Fusion for Autoregressive Multi-Subject Personalization

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A method for multi-subject image personalization that fuses independently trained LoRA modules at inference time on visual autoregressive models.

  6. Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    DisCon treats discrete image tokens as conditioning signals rather than targets, letting a continuous autoregressive model refine details and reach gFID 1.38 on ImageNet-256.

  7. Plug-and-Play Context Feature Reuse for Efficient Masked Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ReCAP interleaves full model evaluations with lightweight steps that reuse cached context features, delivering up to 2.4x faster masked generation with minimal FID loss.

  8. MARch\'e: Fast Masked Autoregressive Image Generation with Cache-Aware Attention

    cs.LG 2025-05 conditional novelty 6.0 of 10

    MARche accelerates masked autoregressive image generation by caching stable token projections and refreshing only attention-selected tokens, reaching up to 1.72x speedup with some loss in FID.

  9. MapBERT: Bitwise Masked Modeling for Real-Time Semantic Mapping Generation

    cs.RO 2025-06 conditional novelty 5.0 of 10

    MapBERT uses a lookup-free BitVAE and a BERT-style masked transformer with object-aware masking to generate complete indoor semantic maps from partial observations in real time.

Pith tools